SenecaLLM has been trained and fine-tuned for nearly one month—around 100 hours in total—using various systems such as 1x4090, 8x4090, and 3xH100, focusing on the following cybersecurity topics. Its goal is to think like a cybersecurity expert and assist with your questions. It has also been fine-tuned to counteract malicious use.
It does not pursue any profit.
Over time, it will specialize in the following areas:
Incident Response
Threat Hunting
Code Analysis
Exploit Development
Reverse Engineering
Malware Analysis
"Those who shed light on others do not remain in darkness..."
Install llama.cpp through brew (works on Mac and Linux)
bash
1brew install llama.cpp
2
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo AlicanKiraz0/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q2_K-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q2_k.gguf -p "The meaning to life and the universe is"
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo AlicanKiraz0/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q2_K-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q2_k.gguf -p "The meaning to life and the universe is"
